Subjects fail to accurately recall events in the past. It's a problem in retrospective studies.

Solution to recall bias

Use multiple sources to confirm information

Late-look bias

Individuals with severe disease are less likely to be uncovered in a survey because they die first

Solution to late-look bias

Stratify by severity.

Confounding bias

Factor being examined is related or influenced by other factors of less interest

Solution to confounding

Do multiple studies and good research design

Case report

Clinical characteristic or outcome from a single clinical subject or event

Case series report

Clinical characteristic or outcome from a group of clinical subjects. Just diseased, no control group.

Cross-sectional study

The presence or absence of disease and other variables in a representative sample at a particular time. Measures prevalence, not incidence. Cause and effect cannot be determined.

Case-control study

People with disease compared to a control group. Almost always retrospective. Doesn't measure incidence or prevalence but determines causality. Qualities of the healthy are compared to qualities of the sick, determines risk factors. Use odds ratio.

Cohort study

Group with risk factor is compared to group without it - prospective. Oppossite of case-control. Measure incidence in each group, determines causality. Most reliable and valid. Use relative risk or attributable risk

Tools used to analyze cohort studies and incidence data

Relative risk and attributable risk

Relative risk

Incidence rate of exposed group / incidence rate of the unexposed group. Greater chance of one group of disease compared to the other group. Used for cohort studies.

Attributable risk

Incidence rate of exposed group - incidence rate of unexposed group. How many more cases in one group. Used for cohort studies.

Odds ratio

AD/BC; where A is the table cell of the object of study and D is diagonally across from it. Chance of risk given disease. Used for case-control studies.

Observational studies

Case, case series, cross-sectional, case-control, cohort

Phase 1 clinical trial

Testing safety of drug in healthy volunteers

Phase 2 clinical trial

Testing protocol and dose levels in small group of patient volunteers

Phase 3 clinical trial

Efficacy and occurrence of side effects in large group of patient volunteers.

Subjects are randomly allocated into intervention and control groups. Most rigorous study. Double-blind is when neither patients nor doctors know which group a patient is in. Least subject to bias, expensive.

Community trial

Entire community is tested

Cross-over study

All subjects receive intervention, but at different times.

Combine probabilities for independent events

By multiplication

Combine probabilities for nonindependent events

Multiply the probability of one event by the probability of the second, assuming the first event occurred

Combine probabilities for mutually exclusive events

By addition

Combine probabilities for events that are not mutually exclusive

Add the two probabilities and subtract the multiplied probabilities

Central tendency values

Mean, median, mode

Mean

Average = sum of the values / number of values

Median

The 50th percentile. The value which divides the set into an upper half and a lower half.

Mode

The most frequent value encountered

Positive skew of the distribution curve

Tail is to the right, mean greater than median

Negative skew of the distribution curve

Tail is to the left, median is greater than mean

Best central tendency measure for skewed distributions

Median

Best central tendency measure for normal distribution

Mean, median and mode are all the same

1 standard deviation

68% of cases

2 standard deviations

95.5% of cases

3 standard deviations

99.7% of cases

Between the mean and 1 standard deviation

34% of cases

Between 1 standard deviation and 2 standard deviations

13.5% of cases

Between 2 standard deviations and 3 standard deviations

2.4%of cases

Above 3 standard deviations

0.15% of cases

Confidence interval

A percentage that assures how much up or down from the sample the true population is.

95% confidence

Z = 2

99% confidence

Z = 2.5

Confidence interval

Mean +- Z (S/square root of the sample size)

Confidence interval for relative risk and odds ratio

If the CI range excludes 1 then it is significant. If the range is above one --&gt; increased risk; if the range is below one --&gt; decreased risk. If the CI range includes 1, then it is not significant

Null hypothesis

The opposite of what is trying to prove. E.g. hypothesis: the drug works; null hypothesis: the drug doesn’t work

p-value &lt; 0.05

Reject the null hypothesis - reached statistical significance

p-value &gt; 0.05

Do not reject null hypothesis - has not reached statistical significance

Type I error or alpha error

Rejecting the null hypothesis when it's really true - asserting the drug works, when it really doesn’t. The p-value is the chance of a type I error - if p=0.05, then chance of type I error is 5%.

Type II error or beta error

Failing to reject the null hypothesis when its really false - asserting the drug doesn’t work, when it does. Cannot be estimated from p-value.

Statistical power

1 - P = beta error

How to increase power

Increase the sample size, which increases power and decreases type II errors

Vocabulary for USMLE Step 1 Biostatistics/Epidemiology. Find, create, and access Epidemiology, Not Set, Time Period flashcards with Course Hero.

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USMLE Step 1 Biostatistics/Epidemiology

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6 terms

Term:

Relative risk

Definition:

Starts with exposure to determine outcome. This is an important measure of strength of association. Is important in establishing etiological relationships, the attributable risk is in many ways more important in clinical practice and public health
How much of the risk (incidence) of disease can we hope to prevent if we are able to eliminate exposure to the agent in question?